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Month: November 2018

No longer is the issue content, we have more content than we know what to do with. The issue is understanding and knowing the contextual relevance of all of that content. And, how to bring relevance to all of that unstructured data, automatically. Which documents are Relevant in context of your Now Subject Specific Informational Need Moment. Consistently Accurate, In Context, Objectively and Automatically.

Applying a Bayesian and Heuristic approach used to be good enough for making general assumptions of category and loose subject relevance of unstructured data. Today, we demand instant, accurate, contextual, objective and relevant results for the information we seek from the Yottabytes of content.

In past, Text Analytic implementations used statistical inference / probability approaches (Bayesian / Heuristics) where lists of keywords and key terms were compiled per subject matter and then referenced to iteratively try and determine what a particular document / data set was about. Those best-efforts results then used to categorize the target content. Similar to how some Document / Content Management Systems work for adding meta data (tags). Usually though, Content and Document Management Systems will require Human Intelligence to first determine how a document being added to a management system should be categorized for a more accurate retrieval purpose.

Let’s hope that Human Intelligence component isn’t having a bad day or the best efforts of objectivity goes out the window.

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